Case study · See Stella

A distributed team cannot hold the same picture in its head.

See Stella is the business we are building ourselves. Its team works across time zones alongside AI agents, and all of them need the same current understanding of what the product does and what the business has learned. So we built one.

What it is for

  • The sync
    What it replaces

    The meeting whose only purpose is telling each other what changed.

  • People and agents
    Who plugs into it

    A distributed team and the AI agents working alongside them, from one source.

  • 600+ notes
    What it holds

    Decisions, learnings and experiments, under six written rules about what counts as true.

  • The agents
    Who keeps it current

    Scheduled agents write the briefing and the metric reading; a weekly steward reviews every note and returns a verdict.

The product team ships. The marketing team writes about last month's version.

When a team is distributed, the shared picture lives in whoever happened to be in the conversation. Someone changes how the product behaves and the people writing about it find out later, or never. Someone runs an experiment and the result stays with them. The same decision gets made twice because nobody could find the first one.

The usual answer is more meetings, which is a tax paid weekly forever. We wanted the alternative: one place holding what is currently true, that everyone reads from, including the AI agents doing real work alongside the team.

Store what is true. Reference what changes.

The brain is a git-tracked knowledge base that agents read from and write to, organised around one keystone rule. Durable knowledge lives as notes. Anything that moves on its own, a metric, a deploy status, a model version, is never written down as a fact. It is stored as a pointer to wherever the live answer lives, and a reading agent fetches it fresh.

That single distinction is what stops a knowledge base rotting. Notes do not go stale because the things that go stale were never put in them.

The effect is that the business stays in step with itself. When the product changes, the corpus changes, and the person writing the next campaign is working from what is true today rather than what was true when they last asked. When marketing learns something from customers, product can see it without being told. Nobody has to remember to pass it on.

The part that makes this different from a well-kept wiki is who does the keeping. The agents are not consuming documentation someone else maintains. They write the daily briefing, take the dated reading of the metrics, report what shipped, and review every note against its own review date. They are bound by the same six rules as the people. Most companies pointing an AI at their internal documents have a read-only relationship with a corpus that is already stale.

Six rules, quoted rather than paraphrased.

  • Never freeze a live number

    Dynamic values point at their source. A note saying “638 of 1,276 scans” is a landmine: the moment it moves, the note lies and nobody notices.

  • Never hard-delete

    Retired notes are archived with a date prefix, never removed.

  • Never silently overwrite

    A changed fact is superseded, not edited. The new note goes live before the old one retires, so there is never a moment with no answer.

  • Every note declares itself

    Status, owner, when it was last reviewed, when it next must be.

  • Ignore what has been retired

    On retrieval, superseded and archived notes are filtered out rather than trusted.

  • Raw goes to the record, not the canon

    Captured material lands as a dated event. Only distilled, confirmed truth is promoted.

It maintains itself on a schedule

  • Daily briefing and digest

    Rolls the day up into one readable place.

  • Metric pulse

    Writes a dated reading of the numbers, as an event rather than a standing fact.

  • Engineering pulse

    What shipped and what is in progress, read from the repository.

  • Usage intelligence

    What people actually did in the product, against what was expected.

  • The weekly steward

    Reads every note, and for each one past its review date returns keep, update or archive.

Any team can write a review checklist. They fail because nothing fires them.

It writes down what it cannot do

The architecture document names its own failure modes, including the unsolved ones. The weekly steward only fires while a particular machine is awake, and that is written down rather than left to be discovered. The current version records that an earlier version described folders which were never built.

Changing how any of it works requires a decision note explaining why, in the same change as the edit. The map cannot drift from the territory without leaving a paper trail.

The product it runs

Stella measures skin over time and reports back: keep using this product, step back from it, or not enough evidence either way. It will not show an insight until there is enough behind it. Those are the same rules the corpus runs on. A company that refuses to state a number it cannot verify builds a product that refuses to claim a result it cannot support.

Who owns it

We do. See Stella was founded in 2023 by Tanuja and Rishabh with a dermatologist co-founder. It is where we work out how an AI-native operation should run before we bring any of it to a client, and it is the reason we can describe this from the inside rather than from a conference talk.

How we implement AI

Where it starts

How much of your week is spent telling people what changed?

We map where the operating knowledge actually sits, and what it would take for the business to hold it rather than the people.

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